This symposium will address the computational challenges of multiple chemical and non-chemical environmental risk factors in environmental health sciences research.
Highlighting this year's event will be a keynote address by Howard Chang, PhD on "Mixture All Along: Statistical Methods for Estimating Complex Exposure-Response Functions". Dr. Chang is Professor of Biostatistics and Bioinformatics in Environmental Health, Rollins School of Public Health, Emory University.
Following the keynote address, at 2:00 pm, the "Michigan Perspectives" SPH faculty panel will discuss topics related to mixtures, including:
- Wei Hao, PhD (Research Assistant Professor, Biostatistics) Statistical methods for chemical mixtures: a roadmap for practitioners
- Michele Peruzzi, PhD (Assistant Professor, Biostatistics) Inside-out cross-covariance for spatial multivariate data
- Sung Kyun Park, ScD (Professor, Environmental Health Sciences & Epidemiology) An environment-wide interaction study to identify hidden environmental factors affecting susceptible populations
Keynote Abstract
Humans are simultaneously exposed to multiple correlated chemical and non-chemical environmental risk factors. Advancements in exposure assessments and statistical tools have enabled a shift towards studying the combined health impact of multiple exposures. This presentation will describe the use of approximate Gaussian process regression and Bayesian additive regression trees (BART) to flexibly characterize exposure-response functions. These approaches aim to address the computational challenges associated with Bayesian kernel machine regression (BKMR) and some parametric assumptions associated with quantile g-computation (qcomp). We apply these methods in several population-based epidemiologic studies to estimate health effects of ambient air pollution on emergency department visits and birth weight in Georgia, as well as an extension to identify heterogeneous health effects of heat waves.
M-LEEaD 2025 Environmental Statistics Day Symposium
Keynote Address by Howard Chang (Emory Univ) Mixture All Along: Statistical Methods for Estimating Complex Exposure-Response Functions
February 24, 2025
1:00 pm - 3:00 pm
1655 SPH I
1415 Washington Heights
Ann Arbor, MI 48109-2029
Sponsored by: Integrated Health Sciences Core (IHSC) of the Michigan Center on Lifestage Environmental Exposures and Disease (M-LEEaD)
Contact Information: mcgehee@umich.edu
This program or event is open to the alumni community
This symposium will address the computational challenges of multiple chemical and non-chemical environmental risk factors in environmental health sciences research.
Highlighting this year's event will be a keynote address by Howard Chang, PhD on "Mixture All Along: Statistical Methods for Estimating Complex Exposure-Response Functions". Dr. Chang is Professor of Biostatistics and Bioinformatics in Environmental Health, Rollins School of Public Health, Emory University.
Following the keynote address, at 2:00 pm, the "Michigan Perspectives" SPH faculty panel will discuss topics related to mixtures, including:
- Wei Hao, PhD (Research Assistant Professor, Biostatistics) Statistical methods for chemical mixtures: a roadmap for practitioners
- Michele Peruzzi, PhD (Assistant Professor, Biostatistics) Inside-out cross-covariance for spatial multivariate data
- Sung Kyun Park, ScD (Professor, Environmental Health Sciences & Epidemiology) An environment-wide interaction study to identify hidden environmental factors affecting susceptible populations
Keynote Abstract
Humans are simultaneously exposed to multiple correlated chemical and non-chemical environmental risk factors. Advancements in exposure assessments and statistical tools have enabled a shift towards studying the combined health impact of multiple exposures. This presentation will describe the use of approximate Gaussian process regression and Bayesian additive regression trees (BART) to flexibly characterize exposure-response functions. These approaches aim to address the computational challenges associated with Bayesian kernel machine regression (BKMR) and some parametric assumptions associated with quantile g-computation (qcomp). We apply these methods in several population-based epidemiologic studies to estimate health effects of ambient air pollution on emergency department visits and birth weight in Georgia, as well as an extension to identify heterogeneous health effects of heat waves.
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